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Several requirements analysis techniques widely used in safety-critical systems are being adapted to support the analysis of secure systems. Perhaps the most relevant system safety techique for Intrusion Detection Systems is hazard analysis.
Human well-being can be greatly impacted by the global environmental and socio-economic change captured in Integrated Models of Global Change (IMGCs). Though most IMGCs address some aspects of well-being, their underlying modeling approach and ‘philosophy’ differ widely, and some key elements – like energy security – are omitted. In this report, we describe a project in which we set out to a create a framework through which the well-being dimensions of the household are connected to key drivers of socio-economic and environmental change – and how the needed metrics, data and modeling methods can be brought to bear. We focus on the well-being dimensions of energy, and lay out the necessary elements to capturing household energy security – using household energy burden as the relevant metric. We begin by showing the conceptual linkage of energy burden to environmental drivers like temperature change, using a simple and straightforward conceptual framework. We then go further to use the example of GCAM-USA to show how some key analytical features of the model can provide insight into how energy security across different groups can change along alternative pathways to sustainability. We compare our preliminary assessment of household energy burden to existing data and suggest further steps to improve and refine this analysis in future research.
The report is delivered as DOE-NE Cybersecurity Program Milestone M2CT-22IN1104014-Industry cybersecurity guidance adoption status. The milestone is specifically to document the process and the status of the efforts towards developing guidance in support of industry’s adoption of wireless communication technologies. To transform the operation of nuclear power plants, including domestic light-water reactors, advanced reactors, microreactors, and fission battery, for grid and non-grid applications, secure and resilient wireless capabilities are required. In addition, to realize new operational concepts such as autonomous operation and remote monitoring, advanced sensor, and instrumentation, wireless technology is essential. However, industry implementation is low, and there is no technical basis for how to understand and address potential risks for wireless communications for critical plant functions. A methodology with a technical basis for implementing secure wireless communication is crucial. This wireless adoption methodology is intended to assist a licensee in identifying an appropriate technological approach for securing wireless communications in nuclear power plant. However, this methodology does not provide guidance for addressing wireless design criteria or for addressing all the cybersecurity commitments in licensees’ cybersecurity plans (CSPs) including addressing security impact analysis that a licensee must perform before an update to a Critical Digital Asset (CDA). This methodology guides a licensee through a process of evaluating a proposed wireless implementation and drafting a plant change notification for an example use case. The example is a generic/nonproprietary version of the first test case of the methodology, which, if successfully implemented, is part of the consideration for protecting other functions’ use of wireless in a cybersecure manner.
Agile Co-simulation for Cyber Energy System Security (ACCESS) is a co-simulation platform developed by Lawrence Livermore National Laboratory (LLNL). The primary high-level use-case for ACCESS is to study existing or new cyber-physical critical infrastructure systems, with a heavy emphasis on 1) systems that utilize communication networks, and 2) studies that seek to understand cyber-related system impacts. ACCESS is currently used for several energy system resilience projects at LLNL. In the Energy Resilience for Mission Assurance (ERMA) project, ACCESS is used in the Mod eling for Metric Calculation task (specifically, subtask 4.3, Communications and Cyber Modeling) to model and simulate the cyber and communication system aspects of Defense Critical Electric Infrastructure (DCEI) systems, with a focus on computing specific communication system metrics that can impact system resilience and mission performance. Simulated communication system per formance will be fed back to other ERMA system components so that mission performance can be evaluated holistically. This report describes several enhancements to the ACCESS platform that were implemented during the execution of the ERMA project in support of reslience analysis. This includes the addition of new models and subsystems, enhancements to existing models, and integration with external systems. The remainder of this report is structured as follows. In Section 2, a brief background description of the ACCESS platform is provided, including an outline of ACCESS components, example use cases, and a set of communication network resilience metrics that can be computed with ACCESS. Section 3 describes the ACCESS model enhancements for ERMA in detail. Finally, Section 4 briefly outlines future integration opportunities between ACCESS and project participant capabilities identified during the progression of the project.
Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.
These maps are an analysis of the Thomas Fire that occurred in California during December 2017. Using a variety of NASA Earth science data from five National Aeronautics and Space Administration (NASA) sources (including four Earth Observing System Data and Information System Distributed Active Archive Centers and NASA Fire Information for Resource Management System), as well as ancillary data from Ventura County, Santa Barbara County, and the Department of Homeland Security, this analysis sought to identify forest fire risk zones, create a fire occurrence density map, examine the vegetation and subsequent burn scar, capture the affected parcels, and capture the affected vegetation.
Battery energy storage systems (BESS) are increasingly important to meet the needs of grid resilience and reliability. BESS provide critical grid services, maintaining stability of the grid with increased variable conditions. However, there are significant geopolitical and security concerns regarding their operation in critical infrastructure, due to lack of a domestic supply chain and prevalence of foreign entity of concern (FEOC) components in BESS and associated inverter-based resources. The supply chain challenge is dually exacerbated by a lack of alternative suppliers who can meet the economic targets for energy delivery and a potentially adversarial supply chain. Solutions are needed to secure components, addressing mixed layers of risk and engineering controls. This paper presents a specific application of Cyber-Informed Engineering (CIE) principles for BESS and recommends an alternative strategy to blocking the supply chain, ensuring that grid modernization targets can be met despite lack of a validated or secure supply chain. This study focuses on the United State (U.S.) use case, but the process can be applied globally to address supply chain security challenges. CIE practices represent the next step in functional assurance and risk mitigation, ensuring optimal resource allocation and enhancing security measures to safeguard the future of energy in the U.S. and beyond.
We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5 σ -level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R [ h - 1 Mpc ] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445 , 0.445 < z ≤ 0.695 , and 0.695 < z ≤ 1.180 , with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample ( 2.1 - 2.4 × 10 14 h - 1 M ⊙ ), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2 , 1.6 ± 0.2 , and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1 σ -level difference in our parameter inferences.
We present a stacked lensing analysis of 96 galaxy clusters selected by the thermal Sunyaev-Zel’dovich (SZ) effect in maps of the cosmic microwave background (CMB). We select foreground galaxy clusters with a 5σ-level SZ threshold in CMB observations from the Atacama Cosmology Telescope, while we define background source galaxies for the lensing analysis with secure photometric redshift cuts in Year 3 data of the Subaru Hyper Suprime Cam survey. We detect the stacked lensing signal in the range of 0.1 < R[h −1 Mpc] < 100 in each of three cluster redshift bins, 0.092 < z ≤ 0.445, 0.445 < z ≤ 0.695, and 0.695 < z ≤ 1.180, with 32 galaxy clusters in each bin. The cumulative signal-to-noise ratios of the lensing signal are 14.6, 12.0, and 6.6, respectively. Using a halo-based forward model, we then constrain statistical relationships between the mass inferred from the SZ observation (i.e. SZ mass) and the total mass derived from our stacked lensing measurements. At the average SZ mass in the cluster sample (2.1 − 2.4 × 10 14 h −1 M⊙), our likelihood analysis shows that the average total mass differs from the SZ counterpart by a factor of 1.3 ± 0.2, 1.6 ± 0.2, and 1.6 ± 0.3 (68%) in the aforementioned redshift ranges, respectively. Our limits are consistent with previous lensing measurements, and we find that the cluster modeling choices can introduce a 1σ-level difference in our parameter inferences.
This safety analysis report for packaging (SARP) presents the results of the safety analysis prepared in support of the Consolidated Nuclear Security, LLC (CNS) request for licensing of the Model ES-3100 package with bulk highly enriched uranium (HEU) contents and issuance of a Type B(U) Fissile Material Certificate of Compliance. This SARP, published in the format specified in the U.S. Nuclear Regulatory Commission (NRC) Regulatory Guide 7.9 and using information provided in UCID-21218 and NRC Regulatory Guide 7.10, demonstrates that the Y-12 National Security Complex (Y-12) ES-3100 package with bulk HEU contents meets the established NRC regulations for packaging, preparation for shipment, and transportation of radioactive materials given in Title 10, Part 71, of the Code of Federal Regulations (CFR) [10 CFR 71] as well as U.S. Department of Transportation (DOT) regulations for packaging and shipment of hazardous materials given in Title 49 CFR.
In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. Here, this study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.
Abstract The past decade has witnessed a rapid rise in the performance of optoelectronic devices based on lead‐halide perovskites (LHPs). The large mobility‐lifetime products and defect tolerance of these materials, essential for optoelectronics, also make them well‐suited for radiation detectors, especially given the heavy elements present, which is essential for strong X‐ray and γ‐ray attenuation. Over the past decade, LHP thick films, wafers, and single crystals have given rise to direct radiation detectors that have outperformed incumbent technologies in terms of sensitivity (reported values up to 3.5 × 10 6 µC Gy air −1 cm −2 ), limit of detection (directly measured values down to 1.5 nGy air s −1 ), along with competitive energy and imaging resolution at room temperature. At the same time, lead‐free perovskite‐inspired materials (e.g., methylammonium bismuth iodide), which have underperformed in solar cells, have recently matched and, in some areas (e.g., in polarization stability), surpassed the performance of LHP detectors. These advances open up opportunities to achieve devices for safer medical imaging, as well as more effective non‐invasive analysis for security, nuclear safety, or product inspection applications. Herein, the principles behind the rapid rises in performance of LHP and perovskite‐inspired material detectors, and how their properties and performance link with critical applications in non‐invasive diagnostics are discussed. The key strategies to engineer the performance of these materials, and the important challenges to overcome to commercialize these new technologies are also discussed.
Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can help practitioners understand the security weaknesses that are frequent in SLPs and adopt adequate mitigation strategies. Objective: The goal of this paper is to help practitioners se-curely develop supervised learning-based projects by conducting an empirical study of security weaknesses in supervised learning-based projects. Methodology: We conduct an empirical study by quantifying the frequency of security weaknesses in 278 open source SLPs. Results: We identify 22 types of security weaknesses that occur in SLPs. We observe ‘use of potentially dangerous function’ to be the most frequently occurring security weakness in SLPs. Of the identified 3,964 security weaknesses, 23.79 % and 40.49 % respectively, appear for source code files used to train and test models. We also observe evidence of co-location, e.g., instances of command injection co-locates with instances of potentially dangerous function. Conclusion: Based on our findings, we advocate for a shift left approach for SLP development with security-focused code reviews, and application of security static analysis.
The use of remotely transmitted data from a nuclear facility under international nuclear safeguards to an inspectorate headquarters has been rapidly growing since inception as its value in reducing inspection effort and cost is demonstrated. There are opportunities for further growth moving forward including (1) the number of spent fuel casks in dry interim storage are increasing, leading to strain on inspection resources and potentially increased radiation exposure to inspectors, (2) the frequency of encapsulating spent nuclear fuel for final disposal in geological repositories occurs at a rate that may lead to the need for on-site inspectors unless systems can be developed to remotely transmit data, and (3) new facility types such as small modular reactors may rely heavily on remotely transmitted data due in part to remote locations of operation and mobility. Challenges need to be addressed too and include (1) hesitancy to implement remote data transmission by states, (2) data collection, transmission, security, and analysis, and (3) reliable power and communications. This report examines the evolution, equipment deployed, status, and opportunities/challenges of remote data transmission moving forward.
Slides created to discuss the report "Approach and Model Used to Represent a Timeline Analysis for Security Design Enhancements" (August 2022 INL/ RPT-22-68664) for an upcoming DOE security workshop. Advanced reactors will be able to use risk insights for many design aspects. We need realistic scenarios for input into the licensing basis safety-case. These scenarios must include timing and physics. We need to automate the safety-case creation as much as possible.
The design of a packet switch incorporating a minicomputer to control and process data packets in a communications system has been reported. This paper proposes a design which uses a 6500 series microprocessor to realize the functions of a message switch in order to evaluate the viability of microprocessors in this application. The proposed design realizes the functions of header analysis, packet security, error control and line prioritization. The system hardware and software are presented together with a brief description of operation.
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